Rethinking Clustered Federated Learning in NOMA Enhanced Wireless Networks
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Abstract (arXiv)
This study explores the benefits of integrating the novel clustered federated learning (CFL) approach with non-orthogonal multiple access (NOMA) under non-independent and identically distributed (non-IID) datasets, where multiple devices participate in the aggregation with time limitations and a finite number of sub-channels. A detailed theoretical analysis of the generalization gap that measures the degree of non-IID in the data distribution is presented. Following that, solutions to address the challenges posed by non-IID conditions are proposed with the analysis of the properties. Specifically, users' data distributions are parameterized as concentration parameters and grouped using spectral clustering, with Dirichlet distribution serving as the prior. The investigation into the generalization gap and convergence rate guides the design of sub-channel assignments through the matching-based algorithm, and the power allocation is achieved by Karush-Kuhn-Tucker (KKT) conditions with the derived closed-form solution. The extensive simulation results show that the proposed cluster-based FL framework can outperform FL baselines in terms of both test accuracy and convergence rate. Moreover, jointly optimizing sub-channel and power allocation in NOMA-enhanced networks can lead to a significant improvement.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 21 of its 53 references that are in the map. First posted on arXiv 5 Mar 2024.
Builds on
21 of 53 references.
References · 21
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- 2020Federated Learning for 6G Communications: Challenges, Methods, and Future Directionscited by 32
9 more
- 2020Federated Learning and Wireless Communicationscited by 27
- 2020Adaptive Federated Learning With Gradient Compression in Uplink NOMAcited by 19
- 2019A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Artcited by 18
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- 2020Scheduling Policy and Power Allocation for Federated Learning in NOMA Based MECcited by 6
- 2021Wireless Federated Learning (WFL) for 6G Networks -- Part II: The Compute-then-Transmit NOMA Paradigmcited by 6
- 2021Mobile Reconfigurable Intelligent Surfaces for NOMA Networks: Federated Learning Approachescited by 3
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.